Implementasi Algoritma K-Means dan C-Means untuk Clustering Angka Kemiskinan

Poverty is one of the problems that must be faced by developing countries, including Indonesia and especially province West Java. This problem exacerbated Covid-19 pandemic. can also have other consequences, such as increased crime death. To facilitate government programs support, it necessary to group cities/districts according poverty level. The analysis was carried out using K-Means Fuzzy C-Means algorithms with Silhouette method obtain optimal number clusters RStudio tools. purpose this study compare which algorithm based on Davis-Bouldin Index validation test. Three five data generated, give same results. Only education different Based results Davies-Bouldin test, fuzzy c-means k-means show better at clustering an average 4.084271. Meanwhile, has score 4.111375. smaller value or closer 0 shows how good cluster is.